Capsule Network Model-Based Fracture Recognition in X-ray Images

In modern medicine, X-ray images are widely used as an important diagnostic tool for detecting and evaluating a variety of diseases such as bone fractures, lung diseases, and tumors. However, traditional X-ray image recognition relies on the experience and expertise of doctors, which involves the risk of misdiagnosis and missed diagnosis, especially in areas with limited medical resources or inexperienced doctors. In order to improve the accuracy and efficiency of diagnosis, the application of deep learning techniques becomes increasingly important. In this study, a capsule network-based fracture recognition model for X-ray images is proposed, and the advantages of capsule networks in capturing complex features of images are verified by training and testing the model on X-ray images from different anatomical regions. The experimental results show that the CapsNet model achieves an accuracy of 84.2% in fracture recognition, which is better than the traditional neural network model, demonstrating its potential and application value in medical imaging diagnosis.